class_docstr: Class for preprocessing Time Series Forecasting Experiments.
class_docstr_tok: ["Class","for","preprocessing","Time","Series","Forecasting","Experiments","."]
code: def _imputation(
      self,
      numeric_imputation_target: Optional[Union[str, int, float]],
      numeric_imputation_exogenous: Optional[Union[str, int, float]],
      exogenous_present: TSExogenousPresent,
  ):
      # Impute target ----
      if numeric_imputation_target is not None:
          self._add_imputation_steps(
              numeric_imputation=numeric_imputation_target, target=True
          )
  
      # Impute Exogenous ----
      # Only add exogenous pipeline steps if exogenous variables are present.
      if (
          exogenous_present == TSExogenousPresent.YES
          and numeric_imputation_exogenous is not None
      ):
          self._add_imputation_steps(
              numeric_imputation=numeric_imputation_exogenous,
              target=False,
          )
  
file_path: pycaret/pycaret/internal/preprocess/time_series/forecasting/preprocessor.py
imports: from typing import Optional, Union
  from sklearn.preprocessing import MaxAbsScaler, MinMaxScaler, RobustScaler, StandardScaler
  from sktime.transformations.compose import ColumnwiseTransformer
  from sktime.transformations.series.adapt import TabularToSeriesAdaptor
  from sktime.transformations.series.boxcox import BoxCoxTransformer, LogTransformer
  from sktime.transformations.series.cos import CosineTransformer
  from sktime.transformations.series.exponent import ExponentTransformer, SqrtTransformer
  from sktime.transformations.series.impute import Imputer
  from pycaret.utils.time_series import TSExogenousPresent
label_desc: Use image node_id 1 for calling the TSForecastingPreprocessor obj's underlying member method code with example usage: obj._imputation(numeric_imputation_target, numeric_imputation_exogenous, exogenous_present) without return types